mirror of
https://github.com/vale981/ray
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248 lines
9 KiB
Python
248 lines
9 KiB
Python
from gym.spaces import Space
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import numpy as np
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from typing import Union, Optional
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.models.action_dist import ActionDistribution
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.exploration.exploration import Exploration
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from ray.rllib.utils.exploration.random import Random
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from ray.rllib.utils.framework import (
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try_import_tf,
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try_import_torch,
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get_variable,
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TensorType,
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)
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from ray.rllib.utils.numpy import convert_to_numpy
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from ray.rllib.utils.schedules import Schedule
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from ray.rllib.utils.schedules.piecewise_schedule import PiecewiseSchedule
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from ray.rllib.utils.tf_utils import zero_logps_from_actions
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tf1, tf, tfv = try_import_tf()
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torch, _ = try_import_torch()
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@PublicAPI
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class GaussianNoise(Exploration):
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"""An exploration that adds white noise to continuous actions.
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If explore=True, returns actions plus scale (annealed over time) x
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Gaussian noise. Also, some completely random period is possible at the
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beginning.
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If explore=False, returns the deterministic action.
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"""
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def __init__(
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self,
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action_space: Space,
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*,
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framework: str,
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model: ModelV2,
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random_timesteps: int = 1000,
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stddev: float = 0.1,
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initial_scale: float = 1.0,
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final_scale: float = 0.02,
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scale_timesteps: int = 10000,
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scale_schedule: Optional[Schedule] = None,
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**kwargs
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):
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"""Initializes a GaussianNoise instance.
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Args:
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random_timesteps: The number of timesteps for which to act
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completely randomly. Only after this number of timesteps, the
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`self.scale` annealing process will start (see below).
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stddev: The stddev (sigma) to use for the
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Gaussian noise to be added to the actions.
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initial_scale: The initial scaling weight to multiply
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the noise with.
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final_scale: The final scaling weight to multiply
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the noise with.
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scale_timesteps: The timesteps over which to linearly anneal
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the scaling factor (after(!) having used random actions for
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`random_timesteps` steps).
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scale_schedule: An optional Schedule object
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to use (instead of constructing one from the given parameters).
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"""
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assert framework is not None
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super().__init__(action_space, model=model, framework=framework, **kwargs)
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# Create the Random exploration module (used for the first n
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# timesteps).
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self.random_timesteps = random_timesteps
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self.random_exploration = Random(
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action_space, model=self.model, framework=self.framework, **kwargs
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)
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self.stddev = stddev
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# The `scale` annealing schedule.
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self.scale_schedule = scale_schedule or PiecewiseSchedule(
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endpoints=[
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(random_timesteps, initial_scale),
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(random_timesteps + scale_timesteps, final_scale),
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],
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outside_value=final_scale,
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framework=self.framework,
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)
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# The current timestep value (tf-var or python int).
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self.last_timestep = get_variable(
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np.array(0, np.int64),
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framework=self.framework,
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tf_name="timestep",
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dtype=np.int64,
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)
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# Build the tf-info-op.
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if self.framework == "tf":
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self._tf_state_op = self.get_state()
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@override(Exploration)
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def get_exploration_action(
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self,
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*,
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action_distribution: ActionDistribution,
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timestep: Union[int, TensorType],
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explore: bool = True
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):
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# Adds IID Gaussian noise for exploration, TD3-style.
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if self.framework == "torch":
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return self._get_torch_exploration_action(
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action_distribution, explore, timestep
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)
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else:
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return self._get_tf_exploration_action_op(
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action_distribution, explore, timestep
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)
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def _get_tf_exploration_action_op(
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self,
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action_dist: ActionDistribution,
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explore: bool,
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timestep: Union[int, TensorType],
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):
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ts = timestep if timestep is not None else self.last_timestep
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# The deterministic actions (if explore=False).
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deterministic_actions = action_dist.deterministic_sample()
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# Take a Gaussian sample with our stddev (mean=0.0) and scale it.
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gaussian_sample = self.scale_schedule(ts) * tf.random.normal(
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tf.shape(deterministic_actions), stddev=self.stddev
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)
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# Stochastic actions could either be: random OR action + noise.
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random_actions, _ = self.random_exploration.get_tf_exploration_action_op(
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action_dist, explore
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)
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stochastic_actions = tf.cond(
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pred=tf.convert_to_tensor(ts < self.random_timesteps),
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true_fn=lambda: random_actions,
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false_fn=lambda: tf.clip_by_value(
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deterministic_actions + gaussian_sample,
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self.action_space.low * tf.ones_like(deterministic_actions),
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self.action_space.high * tf.ones_like(deterministic_actions),
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),
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)
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# Chose by `explore` (main exploration switch).
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action = tf.cond(
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pred=tf.constant(explore, dtype=tf.bool)
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if isinstance(explore, bool)
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else explore,
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true_fn=lambda: stochastic_actions,
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false_fn=lambda: deterministic_actions,
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)
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# Logp=always zero.
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logp = zero_logps_from_actions(deterministic_actions)
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# Increment `last_timestep` by 1 (or set to `timestep`).
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if self.framework in ["tf2", "tfe"]:
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if timestep is None:
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self.last_timestep.assign_add(1)
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else:
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self.last_timestep.assign(tf.cast(timestep, tf.int64))
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return action, logp
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else:
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assign_op = (
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tf1.assign_add(self.last_timestep, 1)
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if timestep is None
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else tf1.assign(self.last_timestep, timestep)
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)
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with tf1.control_dependencies([assign_op]):
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return action, logp
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def _get_torch_exploration_action(
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self,
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action_dist: ActionDistribution,
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explore: bool,
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timestep: Union[int, TensorType],
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):
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# Set last timestep or (if not given) increase by one.
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self.last_timestep = (
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timestep if timestep is not None else self.last_timestep + 1
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)
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# Apply exploration.
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if explore:
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# Random exploration phase.
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if self.last_timestep < self.random_timesteps:
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action, _ = self.random_exploration.get_torch_exploration_action(
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action_dist, explore=True
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)
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# Take a Gaussian sample with our stddev (mean=0.0) and scale it.
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else:
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det_actions = action_dist.deterministic_sample()
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scale = self.scale_schedule(self.last_timestep)
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gaussian_sample = scale * torch.normal(
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mean=torch.zeros(det_actions.size()), std=self.stddev
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).to(self.device)
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action = torch.min(
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torch.max(
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det_actions + gaussian_sample,
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torch.tensor(
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self.action_space.low,
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dtype=torch.float32,
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device=self.device,
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),
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),
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torch.tensor(
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self.action_space.high, dtype=torch.float32, device=self.device
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),
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)
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# No exploration -> Return deterministic actions.
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else:
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action = action_dist.deterministic_sample()
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# Logp=always zero.
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logp = torch.zeros((action.size()[0],), dtype=torch.float32, device=self.device)
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return action, logp
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@override(Exploration)
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def get_state(self, sess: Optional["tf.Session"] = None):
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"""Returns the current scale value.
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Returns:
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Union[float,tf.Tensor[float]]: The current scale value.
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"""
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if sess:
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return sess.run(self._tf_state_op)
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scale = self.scale_schedule(self.last_timestep)
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return {
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"cur_scale": convert_to_numpy(scale) if self.framework != "tf" else scale,
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"last_timestep": convert_to_numpy(self.last_timestep)
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if self.framework != "tf"
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else self.last_timestep,
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}
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@override(Exploration)
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def set_state(self, state: dict, sess: Optional["tf.Session"] = None) -> None:
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if self.framework == "tf":
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self.last_timestep.load(state["last_timestep"], session=sess)
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elif isinstance(self.last_timestep, int):
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self.last_timestep = state["last_timestep"]
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else:
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self.last_timestep.assign(state["last_timestep"])
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